In everyeach
society, there are rich andas
there are poor. The focus of this paper is on the latter category, the poor, whicha
category that grew in Romania in number and severity at
unprecedented levels during transition. Even byunder
a conservative definition of poverty, we found a six-foldtimes
increase in the number of poor during the transition decade. This
paper[1]
investigates why poverty increased so much, and what can be done to alleviate
it.

This increase in poverty is common to many
transition economies, and it is typically considered a by-product of the reform
processes. Most economic reforms have their
victims, despite good intentions. Typically, reforms are implemented to change the current
situation for a better one, judged by some “welfare compensation criteria”.
Some reforms are defended by saying that for a better tomorrow, some should
suffer a bit today. These reforms are usually labeled “macrostabilization”
programs. Such programs are triggered by unsustainable excesses of aggregate
spending over domestic production. The measures designed by economists and
implemented by policy-makers to “restore macroeconomic stability” try to curb
aggregate domestic demand, that means in most cases a reduction in the overall
level of consumption – hence welfare –--,
in that country. A lot of people would see their welfare falling.

Proponents of another type ofOther
reformsarguewould say
that changes that negatively affect some particular groups are beneficial to
the overall economy, because those who gain, gain more than those who loose[i].
These are called “structural adjustment” programs. These programs promote
changes at the
sector level, by restructuring some inefficient operations or by changing
or discontinuing some inefficient practices, with the belief that the benefits
accruing to the rest of the economy are larger than the pain of the “victim”
sector. In this case, some groups whose earnings depends on
the shrinking sectors would see their welfare falling.

Among transition economies, fast reformers succeeded
into
restoringe
their economies on a path of sustainable growth path,
while hesitant reformers did notdon’t.
The link between positive growth and household welfare is trivial. At micro
level, the growing transition economies cured much of the “transient poverty”
and, thanks to sizablesizeable
social budgets
and lower demand for social assistance, alleviated much of the “permanent”
poverty as well. The other transition economies, Romania included, are still
far from this target. We illustrate, in the first chapter, the marginal impact
of “absolute” poverty in countries like the Czech Republic, Slovakia, Poland or
Hungary, contrasted with itsthe
high magnitude and severity in Romania, Bulgaria and republics of the
fFormer Soviet Union Republics,
Romania and Bulgaria. This speaks toabout
the importance of finalizing the reform agenda in Romania, and the desirability
of an increase in the pace of reform marching speed.

MovingLanding
from this regional perspective toin
the situation
in Romanian realities, the paper documents how
much poverty grew in the last decade. If the analysis ended here, Left
to this topic, our paper would add no value-added
to the research
on Romanian research, as 1999 has beenis
a relatively abundant year forin
poverty-oriented studies[ii],
in contrast with the situation two or three years ago. Such relative abundance
of poverty studies raises the question: why more? Apart from its
“regional perspective” on poverty and inequality, we investigate three topics
that are, to a large extent, new to the social and economic research:

·First,
we quantified the contribution of various factors to the household
welfare, and investigated why the poor are poor. Leaving the unobserved or
latent “macro” factors aside, we go down to microeconomic datasets to
investigate what determines household welfare. We build a model to unveil the
factors that are associated with poverty, and to identify the policies that can
help poor get out the poverty pool. We found that those in poverty are those
less endowed with human and physical assets, unable to cope with the income shocks
that have accompanied the transition period. Some of them may exit from the
poverty pool, mainly if their human capital improves. For others, especially
for those lacking labor resources, poverty may be permanent if social safety
nets fail to elevateshield
them from poverty.

·Second,
thanks to new opportunities opened by improved data collection, we
investigate the dynamics of poverty, by looking at the entry into and exit from
the poverty
pool. This exercise is of immediate interest for social policy, because we
identify the groups that were able to cope with the hardship of transition, and
contrast these with the ones that fared worse overin
time. As expected, we found that poor households headed by employees or
pensioners are more able to exit from poverty than the others, notably those whose heads
are eunemployed-,
farmers-
or self-employed-headed households. Social
policy-makers should pay more attention to the latter category.

·Finally,
we investigate the effectiveness of the Romanian social safety net in shielding
the poor
against poverty. Notably, we are asking to what extent social programs tacklehow
much poverty is tackled thanks to social programs,
if the social programs provide adequate coverage to the poor and if the funds
are reasonably well targeted to those in need. We found that the overall social
spending almost halves poverty in Romania, and that most social programs with a poverty
alleviation focus are well targeted to the poor. However, the targeting
technique of the main programs is to addresses narrow
welfare risks such us unemployment, or temporary loss of income due to illness,
childcare or handicap. The only program with a broader focus – the social aid
program – suffers from lack of funds and excessive discretion in
implementation. Consequently, the coverage of the anti-poverty programs in
Romania is inadequate. To illustrate this, we showmention at this
point that the percentage of poor households that do
not receive any support fromthrough
social programs is lowerhigher
than the figure
for those infor the not-poor category. The fact that the
programs are well targeted means that new funds are required to lift the
“post-social programs poor” out of poverty. Given the budget constraints, we
formulate a proposal for the improvement of the social aid program, able to
cure only the extreme poverty.

The paper is structured in five sections.Section one compares poverty and inequality
in Romania with other countries in the region, and the dynamics of poverty and
inequality in Romania. The second section presents a profile of the poor,
including the extreme poor, a “static” household-level-model
of the
determinants of consumption’s (welfare) determinants for
the whole population, as well as tailored models for the largest groups of
poor: employees and pensioners. Section three investigates the dynamics of
poverty, estimating who isare those who areimprovinggetting
better or deterioratingworseoverin
time. We distinguish among transient and permanent poor, two categories whose
escape from poverty would require different solutions. Transient poverty would
shrink when growth resumes, while curing permanent poverty would require social
programs. As manymuch
of the other papers at this conference deal with the issue of growth, we narrow
our focus in section four to the social
sector interventions, especially in their efficacy in poverty alleviation.Section five concludes the paper.

Before beginning an in-depth analysis of poverty and
inequality in Romania, we will step back a little,
and look at these two phenomena over the globe. In particular, we would
ask how manymuch
people live on less than one (international) and four US$ a day in Romania[iii],
and in other regions in the world? And how unequal is the distribution ofn
income,andor
consumption in Romania and in other regions? Such comparison would
reveals the heterogeneity of these phenomena across
the globe.

For international comparisons, we used regional
poverty indicators estimated by the World Bank against a “severe poverty line”,
that counts as poor all persons living on less than one 1985 international
dollar a day.The poverty threshold
used is quite low, signaling malnutrition, the absence of adequate purchasing
power to satisfy the basic food requirements. Poverty estimates for various
regions were produced by the World Bank (World Bank, 1996),
for 1993[iv].
Using the same methodology, we computed a similar estimate for Romania, based
on the 1997 Living Standard Measurement Survey data. For inequality
comparisons, we compared a recent (1997) estimate of the Gini coefficient[v]
for Romania with regional estimates produced by Deininger and Squire[vi]
(1996).

Few Romanians weare
living on less than one US$ a day in 1997, and those who dido,weare
very close to this level (Table 1.1). This means that malnutrition, one of the
cruel signs of poverty, is a very marginal phenomenon in Romania. In this
respect Romania, as well as the other countries in Eastern Europe, Central
Asia, the Middle
East and North Africa, contrasts with the rest of the world: both the incidence and
severity of poverty incidence and severity are low. The
plague of malnutrition is still widespread in other regions of the world, such as
South Asia, Sub-Saharan Africa, East Asia and the Pacific, Latin America and the Caribbean.
Thus, the significant drop in economic activity and welfare registered in
Romania during the “transition decade” did not pulled
the standard of living of those “relatively” poor into the range of malnutrition.
In part, the near-absence of severe poverty in Romania may be associated with a
low level of inequality. Although rising by almost 50 percent from the
“near-equality” levels registered underduring
central-planning,
Romania’s inequality is at the average level of its region (ECA), one of the
lowest in the world.

From an economic policy perspective, a more
interesting comparison would be among Romania and other transition economies,
given the common legacy and reform processes experimented with in the
last decade. To reflect the higher level of per capita income in transition
economies, poverty comparisons should use higher thresholds. Milanovic (1998)
suggested a regional poverty line that counts as poor all persons living
on less than four 1990 international dollars a day, for which he estimated poverty
statistics for Central and Eastern European countries, as well as for the former Soviet Republics, for
various years in the period 1993 to 1995. Using the same methodology, we
computed a similar estimate for Romania, based on the 1997 Living Standard
Measurement Survey data. For inequality comparisons, we compared a recent
(1997) estimate of the Gini coefficient[vii]
for Romania with country estimates produced by Milanovic[viii]
(1998).

Among the countries
from Central and Eastern Europe, Romania has the highest poverty rate but
similar poverty deficit[ix].
Two thirds of the population in Romania are living on less than four PPP$ a day
in 1997, being in average 25 percent below this level (Table 1.2). These two
numbers imply high costs of curing poverty, costs estimated – under perfect
targeting and no administrative costs –at more than 5 percent of GDP[x].
As for income inequality, Romania resembles Poland, Bulgaria, the Czech
Republic or Hungary, with a higher level of inequality than Slovakia or
Slovenia.

The unwanted leading seat Romania’s unwanted
distinctionfor having the highest poverty level in the region holds
inpoverty
level in the region results fromis due to
three factors:a) the pre-transition
differences in income per capita and income distribution;b) the growth performances; andc) the increase in inequality during the
decade of transition in each of the countries. In 1989, Romania had the lowest
income per capita among this group of countries, fairlysomehow
close to the Bulgaria’s (Table A1.1 and A1.2 in
Annex 3). Although,during the
transition decade, all these countries witnessed a dramatic
decline in output during the transition decade, mirrored by a
sharp reduction in the living standards up to 1993, only Romania and Bulgaria
experienced again recessions again in
1997 and 1996, respectively. Finally, while all the countries abandonedleft
central-planning
with low inequality – Gini’s around 0.2 –,
Romania was among the countries with the highest increase in inequality.

There is an trivial
parallel betweenamong
growth performance and success in poverty alleviation. Among transition
economies, fast or committed reformers succeededinto
restoringe
their economies toon a
path of sustainable
growth path, while hesitant reformers did notdon’t.
At the micro
level, the growing transition economies cured much of the “transient poverty”
and, thanks to sizablesizeable
social budgets
and lower demand for social assistance, alleviated much of the “permanent”
poverty as well. The other transition economies, such as Romania, Bulgaria, and
countries from the Former Soviet Union, are still far from this target. We
illustrated, in this section, the marginal
impact of “absolute” poverty in countries like the Czech
Republic, Slovakia, Poland andor
Hungary, contrasted with itsthe
high magnitude and severity in Romania, Bulgaria and the republics of
the fFormer Soviet Union Republics,
Romania and Bulgaria. This speaks toabout
the importance of finalizing the reform agenda in Romania, and the desirability
of an increase in the pace of reform marching speed.

From a practical point of view, anti-poverty
policies are “national” policies, and both poverty measurement and the
instruments for its alleviation are designed relative to the country’s
standards. This is the case in Romania, and in most of the countries in the
world. Although recently the authorities and the domestic research community began to use
systematically a Romanian methodology to quantify poverty, we are captive to
the methodologies used to produce earlier estimates to asses its evolution over
a longer time span.

In Table 1.3 we present the evolution of poverty against a benchmark
pioneered by the World Bank (1997) for the period from 1989
to 1994. Based on this line – equal to a consumption of 46045 ROL per capita in
January 19-95
prices – we estimated the incidence of poverty for the recent years 1995 to
1998. In the first decade of the transition period, the incidence of poverty
increased six times, from 3.7 percent in 1989 to 22 percent in 1998. In the
same table, we present two other indicators that are likely to cause the
estimated increase in poverty: inequality and per capita GDPGPD
growth. During the first six years, the data suggest that the worsening income
distribution did not affected poverty significantly. As expected, the main
determinant of the poverty incidence was the evolution of GDP per capita, the
correlation coefficient betweenamong
the two series being –0.979.

Although Romania does not have an official poverty line, recent studies
used systematically similar methodologies
for poverty measurement systematically (Wagner et al. 1998,
Dinculescu and Chirca 1999, ChircaChirc_
and Tes_liuc
1999), building a national “expert consensus”. Recent studies commissioned by
the “Presidential Commission Against Poverty” endorsed this methodology, tantamount to official acceptance
of
this “expert consensus”making this
“expert consensus” close to an
official acceptance or even support. The core poverty analysis
undertaken in this study appliesis done against
the same poverty benchmark and methodology as the one used in the above-mentioned
studies. The poverty line used in this study is defined as 60 percent of
the monthly average consumption per adult equivalent in 1995, equivalent to US$D
40. For 1996 and 1997, all the monetary variables were deflated to January 1995
prices, to account for seasonal variation in ROL purchasing power. All persons
living in households with a lower level of consumption per adult equivalent
than the poverty line were counted as poor. In Table 1.4 we present poverty
statistics
for 1995-1998 estimated according to this methodology for 1995-98.
The reader interested in the methodological aspect of poverty measurement may
consult Annex 2.

In Romania, both the incidence and the severity of
poverty are high, and have increased over time (see Table 1.4).
Poverty was a marginal phenomenon at the outset of the transition period, but
became a major problem thereafter. Romania experimented with gradual
reforms for almost a decade, a combination of stop-and-gogo-and-stop
policies. These proved to be very costly, so that by 1998,GDPGPD
was still at 76 percent its pre-transition level, with further declines
expected in 1999. A decline in living standards mirrored tThe
decline in economic activity was mirrored
by a decline in
living standards, notably in the level of current consumption per
capita. Poverty was somehow aggravated by the increase in inequality, due
mainly to the new occupational risks,– like
unemployment, and the new opportunities,– such
as the freedom of entrepreneurship. While in 1989 the number of poor in Romania
was estimated to be at around 1 million persons,
in 1998 almost eight million persons or 34 percent of total population were
poor, and more than two millions of
those can be classified as extremely poor.

Poverty in Romania is shallow, meaning that most of
the poor are clustered not far below the poverty line. A low Gini index among the
poor (0.1), and a relatively low consumption gap (the average consumption of
the poor households is only 25 percent below the poverty line) illustrates this.
PDue
to this, poverty is therefore highly elastic to GDP movements.
The 3.9 percent growth in GDP during 1996 broughttook
1.2 million peoplepersons
out of poverty, reducing the poverty headcount from 25.27 percent in
1995 to 19.85 percent in 1996. The 6.6 percent decline in GDPGPD
in 1997 pushed another 2.5 million peoplepersons
in poverty, increasing the headcount to 30.81 percent in 1997. In 1998, the
second year of economic decline when GDPGPD
fell 7.3 percent, the number of poor increased bywith
650 thousand people. Hence, most of these are
“transient poor”, people who have recently become poor due to declining
macroeconomic performance. Restoring growth will take them out of poverty.
Severity of poverty also increased with declines in GDP decline,
doubling in 1998 to 3.56 percent from 1.54 percent in 1996. That means that the
poorest fare worst in 1998, compared with earlier periods.

The overall consumption shortfall is relatively
small as a percentage of GDP is relatively
small, making poverty alleviation policies financially feasible,
at least under a scenarioof
perfect targeting and low administrative cost scenario.
The poverty deficit as a proportion of GDP was 1.5 percent of GDP in
1995, 1 percent in 1996, 2 percent in 1997 and 2.4 percent in 1998. A small
poverty deficit carries a favorable message for anti-poverty policy. Poverty eradication or important
alleviation is possible with a relatively low cost, assuming
perfect targeting. If we take into consideration the phenomena of“leakage” (payments to the non-poor) and
“spillover” (payments to the poor in excess of what they need to reach the
poverty line) and the administrative costs of identifying the poor, the cost of
poverty alleviation might go up. Many writers (Milanovic, 1998; Subbarao, 1997)
emphasized the many difficultiesy
of perfect targeting,, for many
reasons, from lack of administrative capacity, to
the perverse incentives generated by the system[xi],
and recommend anthe
increase of the estimates by a factor ranging between 2 and 3 times. Takingen
an average of these recommendations, we arrive at a cost between 2.5 and 5
percent of GDP, which is substantial.

The next question, answered in the following
chapters, is: MustIs
the cost of covering the poverty deficit supplementadditionalto the cost of existingcurrent
social programs, or can some less effective social programs be eliminated or
reduced to free funds?some of
them can be substituted to
current programs that fare worse in term of poverty alleviation, and that can
be reduced or eliminated. Our analysis suggests that most of this
expenditure would require additional funds. Based on this finding, the final
chapter endorses an intermediate solution, presented in Dhanji et al. (1999),
in which only the extreme poverty is tackled, atwith
an additional cost of 0.33 percent of GDP.

Inequality, as measured by Gini coefficient, is not high in Romania
(Table 1.5).During the eight years of
transition, it increased from 0.21 in 1989 to 0.30 in 1998. However, with a
Gini coefficient of 0.30, Romania compares now with the Western European
economies with a heavy welfare state, well-known for their low level of
inequality. Curiously, inequality decreased in 1997, the first year ofa rapid enterprise reform
program, implemented in parallel with labor redeployment measures. This may be
due to the hesitant path of enterprise reforms in 1997, and the possible
over-supply of the social safety net package. By area of residence, the
inequality was greater in rural areas throughout the transition period.

In this chapter, we
will investigate the socio-economic
characteristics of the households associated with poverty status. The answer would
isbe
provided through simple bivariate statistics in the first section, where we
present differences in headcount rate and consumption shortfall as a function
of socio-demographic, and economic characteristics of the households. In
section two we investigate more formally, using multivariate analysis, the
correlation between household welfare – proxied by household consumption per
adult equivalent – and the level of household resources. The last section of
the chapter uses the same multivariate technique to identify
the factors associated with poverty among the largest occupational groups: employees
and pensioners.

This section builds a poverty profile,
by socio-demographic, economic and regional characteristics.Among the socio-demographic factors associatedion
with poverty are a high dependency ratio, having a female
heading the household, being young or belonging to the gypsy community.
Households would face lower poverty risks if
their endowment with resources is greater, from human capital, productive
assets like land, or consumption assets like durables or houses. Finally, we
signal the regional dimensions of poverty, as we findfound
a higher incidence in the rural area, or in the North-East, South and
South-West of the country. The reader isn
warned to interpret these results with caution, some of them being only
“fallacies of composition”, as the multivariate analysis in the next section would
reveal.

The next subsection presents the main
factors that are associated with poverty, the latter being measured by the
headcount rate and the consumption shortfall. The interested reader may fiound
additional details in Annex 3, Table A3.4.

2.1.1.

Socio-Demographic Characteristics

Dependency ratio (family
size and number of children) Households
composition is one of the most significant correlates of
poverty, since the numbers of income earners and dependentsdependants
determine the consumption needs and the ability to satisfy those needs.
Throughout the world it has been observed that the poverty headcount increases
with household size or number of children. This correlation holds for both
rural and urban areas, and its capacity to discriminate between
low-incidence and high-incidence poverty groups is the highest in any year from
1995 to 1998 (Figure 2.1). Households with five members face more than 50
percent chance of being poor,
and for those with six or more members the odds rise to two thirds. These two
categories account for 50 percent of the total poor in 1998. In contrast,
single- or two-person families face a very low risk of poverty.

Extreme poverty, a concept which
try to identify
those people who face an extremely severe poverty, is also very
high among large families (Table A3.5, Annex 3).In 1998, 19 percent of the households with five members and 35
percent of the households with more than five members lived in extreme poverty.
That means that a year ago, 1.6 million peoplepersons
in Romania were living withless than 40 percentof the average consumption per equivalent
adult.

In most cases, large households include a large
number of children, and thus,
households with more than 3 children have a high probability of being poor.
Families with no children face half the risk of poverty of families with one or
two children.With each additional
child after,the first after
two children, the poverty incidence
increases
by 50 percent, rising to 84 percent in households with four or more children
(Figure 2.1).

In 1998, although the
magnitude of poverty was larger, the relative incidence was similar thanas
in 1995. This means that the position of the poorest relative to the average
remained unchanged.In 1998, the
households with three or more children, accounting for 5 percent of the total
population, have the highest incidence of poverty: 65 percent of the families
with three children and 84 percent of the families with four children or more
are in poverty. Together, these groups account for 17 percent of the total
poor.

More than 30 percent of poor householdswith one or two children and more than 50
percent of those with three orand
more children live in extreme poverty. With each additional childthe chance of living in severe poverty
almost doubles rising to 45 percent in the poor households with four or more
children.The strong correlation between poverty and number of children
in the household makes family benefits (such as the child
allowance schemes) important instruments of safety net policies.

Age.The high incidence of poverty among households with
children determines that one third of all Romania’s poor are children less than
15 years of age. Very popular among
politicians is the idea thatpensioners are the most vulnerable
group.The data in the LSMS provides
quite contradictoryan
opposite evidence.The
incidence of poverty is 18 percent in thegroup aged 56-65 years and only 10 percent in the age
group over 65 years.These two groups together account for only 10 percent of thein
total poor in Romania.

Gender. Our bivariate estimates
show that, over the period 1995-1998, female-headed households face lower risks
of poverty as compared with o their
males counterparts but
larger consumption shortfalls. However, this is simply a “fallacy of
composition”.As revealed by the multivariate analysis
in the next section reveals, male-headed householdswould
have a 5 percent higher consumption, ceteris paribus.Most of
the female-headed households belong to the one-person category, being either
unmarried females or widows. So, on average, female-headed households have
lower dependency ration than male-headed ones. Because the
dependency ratio effect is much stronger, bivariate estimates would show –
erroneously –lower risk of poverty among females.

Even if we consider that the poverty headcount may
accurately measure the risk of consumption poverty that female-headed households are
facing, there are other characteristics of this group that point to its
vulnerability. Most of the female--headed edhouseholdss
are elderly widows living alone, who are faceing
not only the risk of inadequate consumption, but also health hazards.Their needs for care and old-age treatment
are not accurately measured by our indicator[xii].
Given the differentiallife expectancy of women and men, poor women comprise a large
share of elderly women.We consider the
subgroup of elderly, single-female households, as
a group at significant poverty risk.This group represents about 80 percent of the total female-headed
households.

Ethnicity.By ethnicity, the only group whose poverty incidence
departs from the average are the gypsies. The incidence of poverty among
gypsies is 3.5 times higher than the average poverty rate and their consumptionwas 40 percent smaller
than the average consumption per equivalent adult in 1997. Part of this poverty
differential betweenamongthe gypsy
community and the rest has, however, other causes than those
associated with ethnicity, however. Most often gypsy households are of
large size and include a large number of
children, havelow levels of
education and a high propensity towardsinformal activity, factors that are associated with high incidence of
poverty.The other ethnic groups
(Hungarian, Germans, others) have the same incidence of povertyas the Romanians.

2.1.2.

Economic Characteristics of the Households

Human capital. The bivariate analysis
suggests that
the education level of the household head is a significant indicator which
discriminatinge
between low and high poverty (Figure 2.2.). Similarly, the consumption
shortfall decreases monotonically with each additional level of education.

However, the capacity of “education level” to
discriminate between poor and not poor is much weaker than the multivariate
analysis suggests. While the incidence of poverty is highest
among households with no formal schooling is the highest (42
percent), it
isbut not much higher than that of those
headed by individuals with gymnasium or a vocational education. One reason for
this similar poverty incidence is the fact that in Romania very few people are
illiterate, and those who are belongpertain
to the generation aged over 65. This category of elderly, howeverone
the other hand, have had the time to accumulate relatively moreassets (savings), plus
were the main beneficiaries of the process of land restitution (Chirca and
Tesliuc, 1999).The high incidence
among households whose head has vocational education is a consequence of the
fact that typically demand for workers with vocational education declines
during the restructuring.

In terms of consumption welfare, the returns on
education start to be significantly higher only for those households headed by
individuals with post-secondary
or higher education.The bulk of poor,
however, live in those households whose head has only primary or no formal
education (24 percent), gymnasium (25 percent) or vocational education (29
percent).

Occupation. By occupation, the highest
risk of poverty is to be found among households headed by the unemployed-,
farmers-or theand
self-employed-headed households (Fig. 2.2).Employee and pensioner households are bellow
the average risk of poverty; however, these two groups amount for two thirds of
total number of poor in all the four years.Similarly, the consumption shortfall issignificantly larger than average for the unemployed, farmer and self
employed households, and smaller for the employees and pensioners. Some new
occupations that emerged during transition (e.g. non-agricultural self-employment) are associated with high poverty
risks, a situation that is atypical in the region
(Grootaert, 1998). Unemployed-headed households face the highest incidence of
poverty, although their relative position improved each year since 1996.

Ownership of assets Households with more than two hectares of land have
lower poverty rates than the average (Fig.2.3). At this stage it is
difficult to determineappreciate
if land ownership influences welfare, while accounting forwithout keeping
under control other factors.Most often the ownership of land is of small size and is associated with
pensioner type households and
of small size.The ownership of land does not make much difference in the
consumption shortfall.This is because those who do not own land may own other important
assets such as education or work experience.

2.1.3Regional Aaspects
of pPoverty

There are two main regional dimensions of poverty in
Romania. First, poverty incidence differs markedly between rural and urban
areas. Throughout the period, rural poverty was about 50 percent higher that
the urban one. For instance, in 1998 about 41 percent of the rural population
lived
in poverty, while in the urban areas only 28 percent of the inhabitants were
poor.

Second, there are differences in the poverty levels betweenamong
regions, as emphasized synthetically in Figure 2.3. The figure is based onto
a very dis-aggregated definition of regions
(15), presented in the Green Paper on Regional Development (Government of
Romania, 1995) higher than the “official” one (8).From an institutional point of view, Romania was “divided” into 8
development regions which, for most development indicators, are not homogenous.
Such classification would be of little use for our purpose, because it will
hide the higher intra-regional differences. The LSMS allows us to
compute poverty at the level of 15 statistically representative sub-regions,
identified in figure 2.3 by the initials of the component judets. The poor
regions are North East (Botosani, Vaslui, Iasi), South (Teleorman, Giurgiu,
Ialomita, Calarasi) or South West (Valcea, Gorj).

The basic model estimated in this section is a
variant of those presented in Braithwaite and Grootaert (1998) and Chirca and
Tesliuc (1999). ThisSuch
welfare model is a reduced form equation of the various structural equations
which express the income-earning and consumption behavior of the households
(see e.g. Glewwe, 1991). The model starts from the observation that household
consumption – a widely used proxy for household welfare – is determined, first,
by the level and quality of the resources a household owns, and second,
by the returns that household may derive from these resources. To compare the
consumption of households with different sizes and demographic structures,
consumption was expressed in an “adult equivalent” measure. In addition, some
control variables such as family size and the gender of the
household head were introduced as right-hand side variables.

The simplest form of model to be estimated is:

where the dependent variable C is household
consumption expenditure (per adult equivalent), HC and PR are vectors of
household resources (measuring human capital and,respectively,physical and financial capital, respectively),
R is a vector of location variables that may influence the rate of return on
resources HC and PR, and a, b and d are coefficients to be estimated. e is the disturbance term.

The dependent variable in the model, C, is measured
in natural logarithms. Current consumption includes consumption of food,
non-food and services. This variable was regressed on three types of variables
(HC, PR and R) which are explained as follows:

1.Household human capital variables. The first block of
variables measures the human capital available at the household level. Human
capital is usually measured by the level of education, experience proxied by
age, the sector of activity and occupation. For education, we used as predictor
the average years of education of adult members. Experience was proxied by the
age of the household head. For occupation and sector of activity, we used the
number of wage-earners, farmers, pensioners, unemployed and employers in the
household. One would expect higher consumption be associated with higher level
of education and experience. As for the occupational status of the active
members of the household, one would expect consumption to be positively
correlated with earning capacity. The magnitude of occupational coefficients
will be used to rank the impact of various occupation on the expected level of
consumption per adult equivalent.

2.Household physical resources variables. The second block of
variables measures the physical and financial resources the household owns, and
includes livestock, area of land owned and the stock of savings. For livestock,
the predictor used is the level of the stock measured in large cattle units[xiii].
The amount of agricultural land owned was expressed in hectares, and the stock
of savings at the end of the period in ROL. One would expect a positive
correlation between the availability of land, livestock or savings and
consumption.

3.Regional variables. Household consumption may vary regionally, due to
unobserved location-specific variables.To account for these, we introduced a vector of dummy variables for each
region of Romania (eight regions), plus one for the area of residence
(urban-rural)

Other variables introduced in the model, contained
in a residual block, includeare
the size and demographic structure of the household, the latter being measured
by the age and gender of the household head. Household size, expressed in
equivalent adults, was introduced to control the economies of scale associated
with larger households. The demographic structure of the household controls for
the fact that consumption varies with age and gender.

Information on household welfare and
privately-owned, household resources have been collected through the Integrated
Household Survey (LSMS), the 1998 Archive. The estimated model is presented in
the Tables A3.6 and A3.7, Annex 3. The model, estimated through least-squares
and corrected for the heteroskedasticity of the error term[xiv],
is highly significant (probability of F-statistic is lower than 1 percent) and
has high explanatory power.The independent variables are able to capture 43 percent of the
variation in the consumption per adult equivalent. Such goodness of fit
isconsidered good for a cross section.

The log-linear functional specification was chosen
over the linear form on the basis of the Davidson and MacKinnon test (1981).
This impliesy
that effects of household characteristics on welfare are proportional rather
than linear, and the elasticity of consumption on various predictors would be a
linear function of the value of that predictor. More simply, the elasticity
would be, in absolute terms, lower for the sample of poor than for the sample
of not-poor. To explore this feature of the model, we estimated the elasticity
on each predictor at mean value for poor and not-poor. For not-significant
predictors (at 5 percent confidence interval) we dido
not estimated elasticity. The results are
presented in Table 2.1. For dummy variables, were computed marginal changes
(effects), indicating the percentage change in (natural logarithm of)
consumption relative to its mean when the indicator variable changes from zero
(e.g. rural, for the area of residence variable called Urban) to one (urban).

These elasticity coefficients are “marginal returns
on the available resources”, showing how much consumption per adult equivalent
would change when the independent variable would changesbywith
one percent.All elasticity isare
below one, meaning that a one percent change in the (mean) level of any
predictor would change consumption by less than one percent.The variable with the biggest impact on
consumption is family size, confirming our findings from the previous section:
one percent change in the number of adult equivalents would reduce consumption
by 0.545 percent.

Second in importance is the “average years of adult
schooling”, which shows that a one percent change in schooling would increase
consumption by 0.32 percent. A different specification, using interaction
dummies for area of residence, yielded 50 percent greater returns from
schooling in urban versus rural areas.

is much lower for the rest of the variables. The
next most important variable is “number of employees” (elasticity 0.11)
followed by ownership of physical assets like land or livestock (0.04-0.03) and
increase in the number of other income earners. In line with earlier findings,
the presence of a single farmer in the family would not change significantly
its consumption, while an increase in those who are unemployed would reduce it.

Second inas
importance iscomes
the “average years of adult schooling”, which shows that a one percent change
in schooling would increase consumption by 0.32 percent. A different specification,
using interaction dummies for area of residence, yielded 50fifty
percent greater returns from schooling in urban versus rural areas.

Elasticity is
much lower fFor
the rest of the variables, the
elasticity are much lower. The next most
important variable is “number of employees” (elasticity 0.11) followed by
ownership of physical assets like land or livestock (0.04-0.03) and increase in
the number of other income earners. In line with earlier findings,
the presence of a single farmer-only
in the family would not change significantly its consumption, while anthe
increase in those who are unemployed would reduce it.

An increase in the human or physical capital of a
household would increase more than proportionally the
welfare of a not-poor household, compared to the poor ones.The highest differential is on the stock of
savings (32 percent, due to the differences in average endowments), followed by
changes in household occupational structure (change in the number of employees,
– 38 percent;,
social security pensioners,– 48
percent;,
and farm pensioners,– 58
percent). Significant differences in returns between poor and not-poor exists
on the income derived from land (71 percent), although not for livestock (94
percent).As expected, anthe
increase in the number of unemployed persons in a family would affect more
than three times a poor householdmore , than
a not-poor one by more than three times (368 percent).

Gender of the household head and location impact on
household consumption. Controlling for all other variables, male-headed
households enjoy greater average consumption thant
similar, but female-headed ones.Such
gender differential is estimated at 5 percent.By area of residence, urban households have antheir
average consumption per equivalent adult 4.7 percent higher that those located
in rural areas.By region, the highest
consumption – ceteris paribus – is in the capital city, Bucharest, and the
lowest in the North-East, North-WVest
and WVest.

The two largest groups of poor are those living in
households headed by employees (39 percent) and pensioners (26 percent),
despite the relatively low poverty incidence of this type of households. We
investigate, through regression analysis, the characteristics of households
headed by employees or pensioners that tend to be associated
with poverty based on the LSMS 1998 sample. The estimations are presented in
Tables
A3.8 throughto
A3.11 in Annex A3. The characteristics that tend to have a significant
influence on the poverty status of these groups are presented bellow.

Employee households.The most important factors associated with the poverty
risk are those regarding the household composition: first of
all, the morehigher
vulnerable households tend to be those with a larger size (i.e. number of
members adjusted into adult equivalent); second, an increase of the dependency
ratio (defined as number of children divided by number of permanent income
earners[xv])
as well as the presence of unemployed members in the household raise
significantly the odds of being poor; finally, the gender of the household head
is another factor that has a relatively strong influence with respect to
poverty: female headed households are more likely to be poor than the male
headed ones.

A second category of factors influencing the
poverty status concernsis the one
regarding the educational level and job-related characteristics of
the household head. EThus, one can
observe that each additional level of education achieved by the
household head has a very strong diminution effect on the household’s
probability of being poor. With respect to job-related characteristics,
households headed by employees with higher professional status (manager,
professional, technician etc.) are less vulnerable to poverty risk than
households headed by workers, especially unskilled ones. An interesting finding
is that although the average level of welfare (measured by consumption per
equivalent adult) of private sector employees does not differ significantly by
comparison with thateone
of state employees, the former category is more likely to be poor
(ceteris paribus). A possible
explanation of this phenomenon could be the higher inequality of welfare among
private sector employees as compared with the state ones, as an effect of wages
inequality. As far as the sector of activity of the household head is
concerned, one can observe that the households headed by employees in
agriculture or education are more likely to be poor, while those headed by
employees in extractive industry or energy, gas and water (characterized mostly
by a monopoly position) are the less vulnerable to poverty risk. Also, it is
important to say that an unstable position of the household head on the labor
market (i.e.,
having a limited period contract) increases significantly the odds of being
poor.

Finally, there are two more relevant factors
relevant tofor
the poverty status of the employee-headed households: the first is the
ownership of land, which has a high positive impact on reducing the probability
of being poor;,andwhile
the second concerns the impact of regional labor markets,and
showings
that households located in judets with higher rates of unemployment are more
exposed to poverty.

Pensioners households. Pensioner households have
the lowest incidence of poverty, after the employeers
ones.The most important factors that
influence their poverty status are, as in the case of employee households,
those related to the household composition: larger households are more exposed
to the risk of poverty, as well as households with unemployed members.On the other side, having employees in the
household or being male headed by a male lower
the household vulnerability to poverty. The education of the household head has
also has a strong impact on the poverty status:
those without schooling or with lower levels of
education are the most likely to be poor. The type of pension received by the
household head is another important determinant of poverty for pensioner households –
- the odds of being poor are greater for those collecting in
the case of agricultural, disability and survivor pensioners than for those
collecting being higher as compared with old age
state insurance pensioners.

From a geographical point of view, one can observe
that the probability of being poor is unevenly distributed among pensioner households,
the regions North-East and West being the ones with the highest risk of
poverty, as compared with Bucharest. Finally, it is worth being
mentioninged
that, despite the large impact of land ownership on reducing the vulnerability
to poverty, the rural located pensioner households are more
likely to be poor (ceteris paribus)
than the urban ones.

Conceptually, there are two situations that makes
a person’s
welfare falling below the poverty threshold: an
income shock, or a low level of assets with growth-inelastic returns. Income
shocks may impoverish households temporarily. In the poverty literature, such
households are called the transient poor. They would escape poverty even
without outside help, after a period that is proportionate with the fall in
income caused by the income shock and the return of the assets (including
labor) they own. In this category one would include the unemployed who, in
periods
of economic recession, loose their jobs. When the economy
recovers and employment increases, such individuals would re-enter
the labor force and may escape poverty.

Other households would not be able
to escape poverty even when economy recovers, because the assets they own do
not generate sufficient income to lift them over the poverty threshold. The
meager volume of assets such households’
own have similar returns under boom periods as under recession. Such households are
called permanent poor. Typically, in this category are included the disabled, andor
poor elderly unable to work..

The distinction among transient and permanent poor
is not straightforward. Some authors (Rashid, 1997) consider as permanent poor
those who, for some period of time, do not escape from the poverty pool. Other
authors (Sen, 1976) consider as permanent poor individuals without the capacity
to adjust and exit from the poverty pool, irrespective of the fact that such
assumed capacity was exercised or not. The first classification is based on the
dynamics of poverty, and is relatively simple to measure.The second classification is more close to
phenomenon concepts, andbut
requires subjective assumptions forto allow
quantification. We estimated both of them, as they look at poverty from
slightly different angles, thus bringing thus additional
insights toon
the poverty processes .

Poverty Dynamics During Transition. To investigate the
dynamics of the poverty in recent years, we used
the subsample of the LSMS from 1995 to 1997.Out of roughly 32,000 households surveyed each year, we selectedfound
– thanks to the rotating panel feature of the data – about 3,000 households fromsurveyed
each year. We used the panel to quantify the entry into and exit from the poverty
pool, and to test if past poverty is associated with current poverty. OfFrom
all the individuals in the panel, two thirds (63.6 percent) were not poor
during the period (Table 3.1). The rest of the households were poor in at least
one year.

We would divided the
households that were poor forin
at least one year into three groups.First, those that were poor throughout the period, fitting our first
definition of permanent poor. Second, those who exited from poverty in good
years (1996), butentered in bad years
(say, 1997). This group is close to our definition of transient poverty. The
remaining group contains the exemptions, households that either fell into
poverty when the economy went well, or exited from poverty in periods of
recession. We have called them atypical poor. We presented,
in Table 3.2, the structure of the subsample of the “poor forat
least in one year”, for the whole subsample
and disdiss-aggregated
by the occupation of the household head in 1997.

Judginged
by the dynamics of those suffering poverty, most of it – 60.7 percent – is
transient poverty.Surprisingly, permanent poverty is only 17.9 percent,
significantly lower than the atypical poor (21.4 percent). Theise aggregate
dynamics concealhide
significant behavioral differences betweenamong
various types of households, grouped by the occupation of the household
head.Extremely low levels of permanent
poverty are noted for the employee and pensioner-headed households.Such households seems to be able to restore
their consumption above the poverty level in one or two years after the income
shock. In contrast, a large proportion of theself-employed or farmer-headed households “at least once poor” bear their
poverty stigma year after year. This exercise is of immediate interest for
social policy, because we identified the groups that were able to cope with the
hardship of transition, and contrast these with the ones
that fare worse overin
time. As expected, we found that poor households headed by employees or
pensioners are more able to exit from poverty than the others, notably the
unemployed-, farmer- or self-employed-headed households. Social policy-makers
should pay more attention to the latter category.

As a feature of the transition process, one would
notices the relative stability of the proportion of
“atypical poor” for all types of households. In our opinion, this is indicative
of the transformations that occur in the real economy – whichthatimpactaffects
primary market incomes such as wages and entrepreneurial income –,
but also in the cash benefit system.

Various hypotheseis
may be formulated to explain the high turnover into and from the poverty
pool. First, the shallow poverty in Romania, plus the arithmetic of poverty
measurement may be responsible for such results. About 8 percent of Romanians
were 5 percent above or below the poverty line in 1997. Relatively small income
shocks may have
a
small
impact on their welfare position,changetheir welfare position a little, but still be enough this
little is sufficient to
change their poverty status. Second, during the three years under analysis thereit
was an abundance of income shocks, a situation that is common in transition
periods. As mentioned already, the enterprise reforms impacted on
the wages and the welfare of some employee-headed households, as
well as on those affected by the subsequent redundancies. The containment of the
aggregate demand reduced the profitability of many businesses, hurting
for instance the self-employed. Changes in the parameters of social programs,
such as the lack
of timely readjustment of pensions, child allowances or social aid, produced
another type of shocks. Although such hypotheseis
may be plausible, we caution the reader that they were not demonstrated, and
their validation or rejection requires further research.

The Poor That Needs Our Help. The second means for classifyingicationtheof
poor into transient or“permanent” poor uses as its criterion
for determining the permanent poor the
lack of means to overcome poverty. Here, we
includeclassify
as permanent poor the disabled, some of the elderly and families
with a large numbers of children (four or above). According to
this criteriona,
about 28 percent of the poor were in permanent poverty in 1995 and 1996 (Table
3.3). As expected, in 1997 and 1998, years of economic decline, this proportion
dropped to 24 percent as an effect of the large entry of “transient poor”. This
definition gives us higher estimates than the “dynamic” interpretation, but in
a relatively smaller range. In the end, it
carries the same message, that much of the poverty in Romania is transient in
nature.

In this chapter we investigate the effectiveness of
the Romanian social safety net in shielding the poor against poverty. In particularNotably,
we are asking
how much poverty theis tackled thanks
to social programs alleviate, if the social programs provide
adequate coverage to the poor and if the funds are reasonably well targeted to
those in need. Also, we will try to assess the role of the state in reducing the
inequality through redistribution.

We found that the overall social spending almost
halves the poverty deficit in Romania, and that most social programs with a poverty-alleviation focus are well targeted to the poor. However, the main
programs address only narrow welfare risks such us unemployment, or temporary
loss of income due to illness, childcare or handicap. The only program with a
broader focus – the social aid program – suffers from lack of funds and
excessive discretion in implementation. Consequently, the coverage of the
anti-poverty programs in Romania is inadequate. IndeedTo
illustrate this, we mention at
this point that a lowerthe
percentage of poor households in the poor
category that do not receive any
support through social programs than do households in is higher than
for the not-poor category. The fact that the programs are well-targeted means that additional funds are required to lift the
“post-transfer poor” out of poverty. Given the budget constraints, we formulate
a proposal for the improvement of the social aid program, able to cure only the
extreme poverty.

This chapter draws heavily on a recent analysis of the Romanian
social protection system done by Dhanji et al. (1999).

Romania has a large array of social protection programs, both (cash and in kind).The cash transfers programs (which number more
than 30) are the most important in terms ofas
funds employed. A matrix-format description of all cash transfers is presented
in Annex 4. Synthetically, they can be grouped into three broad categories[xvi]:

Social insurance: benefits awarded on the basies
of a social insurance contribution record, or at the occurrence of a specified
contingency, like unemployment, sickness or old age.Social insurance includes pensions, work injury insurance,
sickness benefits, unemployment insurance, severance payments,
maternity and child care benefits. They formcut
the largest share of totalin the total
spending on cash transfers. In 1997, they accounted for 81 percent of the total.

Entitlements: universal benefits awarded on the basis of
categorical characteristics, not related to the income of the recipients or
contributions
to benefits
schemes.
Entitlements include child allowance, incremental child allowance and birth
grants. In 1997, they accounted for 14 percent of total spending on cash
transfers (Fig. 4.1).

Social assistance: benefits awarded for people in “need”,
based on a means test or the occurrence of an emergency situation. Social
assistance includes social aid for low income families (an MIG-type program),
allowance for wives of conscripts, family allowance for child placement,
emergency help, allowances for prosthesis procurement, allowance for thermal
energy, compensation for the increase in bread price, special aid for the disabled.
In 1997, these programs accounted for less than 5 percent of total spending on
cash transfers.

Many of these programs are inherited from the
centrallyplanned regime, and have begun
to be reformed only recently (e.g.,:
pensions). Among the new programs, unemployment benefitswerewas
introduced in 1991, and the minimum income guarantee in late 1995.The child allowance
benefit, whichthatduring
central planning during only covered the
employees’ childrencentral planning covered only the employees’
children, was transformed into a universal benefit in 1993.

The structure of the Romanian social safety net has
a heavy component of social insurance. In this respect, Romania is similar with
most countries in the world. Somewhathow
peculiar is,after 1997, the
large reliance on entitlements to support households after 1997.
The social assistance programs, targeted toward those in need, play a minor
role in the total spending envelope of the cash transfer system.

By type of program,
Tthe
most important program by type one is
the pension programs.
In the four-year period covered in the paper, pensions represented between 65
and 80 percent of total cash benefits, or around 6 percent of GDP. In 1997,
they accounted for 65 percent of total spending (Figure 4.1). Besides pensions,
programs with sizable budgets are the child allowances and the unemployment
benefit, with 0.6 to 0.7 percent of GDP. In 1997, their share in total cash
spending was 14 and 12 percent, respectively. The social aid (MIG), the only
social assistance program conceived as an antipoverty program with payments being
made on the basis of need, provides the smallest cash transfer (less
than 1 percent of total cash benefits in 1997). The overall cost of the cash
transfer programs wasranged between
9.2 percent of GDP in 1997, upraising
from a low of 7.7 percent in 1995.

The majority of Romanian households receive at least one cash transfer
from the state (Table 4.1). Between 1995 and 1997, the percentage of households
serviced by the cash transfer programs ranged from 79 to 82 percent. In terms
of beneficiaries, the cash transfer reached between 45 andto
46 percent of all individuals (not counting those that benefited from transfers
designated to households, not individuals).

Programs like pensions and child allowance were, as
expected, remarkably stable. Significant changes occurred only in the coverage
of the unemployment benefit and social assistance. Our estimates
mirror tThe reduction in unemployment in 1996
is mirrored by our estimates. In 1995, 3.3 percent of individuals
cashed in unemployment benefits. After 1996, only 2 percent were still cashing
in unemployment benefits, in line with the fall of the unemployment rate and the
exit of some long-term beneficiaries from the list of assisted persons. Social
assistance covered a much larger share of the households in 1997, compared with
the previous two years, an increase of 6 to 8 times in the number of assisted
households.This was due to the
introduction of some temporary assistance programs, such as the “compensation
for the raise of the bread price”, a benefit introduced forin
several months in 1997 (March to October), that was distributed to a much
larger number of households than the existing programs.

In this section, we investigate how efficient is
the Romanian cash transfer system is in alleviating poverty. Among the
transfer programs, some of them have a poverty alleviation objective and others do not.
Our investigation will be limited to programs that have poverty reduction as a primary
or secondary objective, excluding, for instance, pensions.

How many people would be poor without the current system of cash
transfers? The success of any social transfer system is measured by the extent
to which it contributes to poverty alleviation. Figure 4.2 illustrates this
using two poverty measures, the headcount rate and the poverty gap index
(FGT1).

The cash transfer system reduces poverty
substantially, even when pension impact is not taken into account. In the
absence of all cash transfers (pension included), poverty rate would increase
by 70 to 96 percent, from 25.3 to 45.3 percent in 1995, 19.9 to 39.3 percent in
1996 and 30.8 to 52.3 percent in 1997. Much of this increase in the number of
poor, between 70 and 80 percent, would occur in the absence of pensions, or if
pension replacement rates would deteriorate substantially. However, pensions do
not have poverty
alleviation as their explicit function poverty
alleviation, thus we will not analyze them from a poverty
alleviation point of view. In the absence of all cash transfers but pensions, the poverty
rate would increase by 17 to 18 percent, from 25.3 to 30.4 percent in 1995, 19.9
to 23.8 percent in 1996 and 30.8 to 36.9 percent in 1997.

The cash transfer system has an even a
greater impact in reducing the overall poverty gap. The headcount
rate tends to underestimate the poverty alleviation impact, as it does not take
into account the fact that transfers may also alleviate the severity of poverty
forof
those still poor[xvii]. Another
measure, poverty gap (FGT1), is sensitive both to the depth and incidence of
poverty. The second graph on Figure 4.2 illustrates the dynamics of the poverty
gap with and without transfers. In the absence of all cash transfers, the poverty
gap would increase by 2.8 to 3.8 times, much more than the number of poor. As
before, much of this increase in the poverty deficit would occur in the absence
of pensions, or if pension replacement rates would deteriorate substantially.
In the absence of all cash transfers but pensions, poverty gap would increase
by 45 to 47 percent.

What cash programs, excludingbut pensions, had the
greatest contribution to poverty alleviation? Compared with athesituation ofno cash transfers except pensionspre-transfers
but pension situation, the two programs with the
largest impact on poverty alleviation are the child allowance and the
unemployment benefit. These two programs tackled most of the potential poverty.
Between 1995
and 19-96, unemployment benefits reduced
the pre-transfer (but pension) headcount by 5-6 percent, and child allowances
by 6-7 percent. In 1997, the upward adjustment in the child allowance resulted
in an increased contribution of this instrument in poverty alleviation, at the
expense of the unemployment benefit and other, non-contributory programs. As
illustrated in Figure 4.3, the head count reduction determined by child
allowance was even larger for 1997, atbeing
about 10 percent. On the other side, social assistance, the only set of
programs with a
clear poverty alleviation mandate, contributed little to its goal. From
1995 to 1997, the rise in (current) poverty headcount associated with an
absence of all social assistance spending would be less than half a percentage
point.

Cash transfer programs in Romania do not seek high
coverage of poor, but are rather “specialized” in some poverty risks. The concernissue
with such a system
is that many poor may fall between these programs,
and notwould
be not assisted. As an illustration one
could note that while in 1997 the share of the (post-transfer) non-poorhouseholds that do not
receive any cash transfer is about 16 percent, the corresponding figure for the
poor household is higher, being around 19 percent. Tables A3.11 and
A3.12 in Annex 3 documents the impact of some cash transfer
programs on the reduction of the number of poor and the poverty
gap for 1997. For instance, unemployment benefits reduces
substantially the poverty deficit among households headed by unemployed, while
child allowances do the same for young households,
with many children[xviii].
As mentioned above, social assistance has only a marginal impact, significant
for households
with illiterate adults, or living in overcrowded houses[xix].

When trying to evaluate the impact of a program on
the poor, one must take into account three essential dimensions: a) coverage–-
the share of pre-transfer poor households that are covered by the program, b) targeting–-
the share of funds that are transferred to the poor, and c) effectiveness–-
the share of the benefit in the average consumption of the poor recipients. A
simple and intuitive way to present all this information is illustrated in
Figure 4.4 for 1997 data. In the graph, program coverage of poor is measured on
the X axis, program funds’ targeting of theon
poor is measured on the Y axis, and program effectiveness – the share of the
programs’ benefits in the consumption of poor beneficiaries – is proportionate
with the size of the “bubbles”[xx].
Take as example the bubble that stands alone in the middle of the graph, on
child allowances. This instrument covers 60 percent of the poor households
(read X axis), transfers 51 percent of the funds to (pre-transfer) poor families
(read Y axis), and representscontributesoin
average with 11 percent ofinthe total
consumption of the poor recipients (read the “size” of the bubble).

As Figure 4.4 illustratesd
in Figure 4.4 that,
social programs falls into three categories, if judged by their
coverage and targeting.First, there
are three programs that have little coverage and targeting, that can not be
justified from a poverty alleviation point of view: the indemnity for
politically persecuted, for war veterans and their widows, and
scholarships. Fortunately, these programs are minor expenditure items and,
except scholarships, have a different social goal, to reward citizens with
special merits and their dependents. The efficiency of the scholarship program
is questionable. MIn the design
phase, most of the scholarships are designed to be needs-basedfulfill
social function, with only few of them being awarded for “merit”, a variable
that might be unrelated towith
the income of the recipient. In practiceEx-post,
however, most of the scholarships (73 percent of funds in 1997) went to
non-poor, while only 27 percent reached the poor, a figure that is much lower
than the share of poor in the total population (30.8 percent).
Scholarships seem to have a regressive distributive impact, in favoringof
children from middle to rich class. It seems that a thorough
review of this instrument is desirable, from eligibility through screening up
to benefit delivery, in order to improve its pro-poor impact for the poor.

Second, there are six programs that have good
targeting, but relatively low coverage of the
poor: the minimum income guarantee scheme, the unemployment benefit, the social
pension, the sickness benefit, the maternity and child care benefit and the
special aid for the disabled. These instruments each targetedpoor households between
54 to 79 percent of the timeto poor
households, and covereds
between 1 and 13 percent of the poor in 1997. Three of them cover rare social
risks (1 percent of the poor), such as maternity, child care, lack of earning
ability due to old age, or sickness, and cover a sizablesizeable
portion of thepoor’
consumption
of the poor. Similarly, the aid for the disabled cover 4 percent of the poor,
target 62 percent of funds to this group and contribute with 17
percent to the consumption of beneficiaries. Next, the unemployment benefit
performs very well, covering 13 of the total poor population, targeting 68
percent of the program expenditures to this group and contributing with
21 percent to the average consumption of poor beneficiaries.
Finally, social assistance recorded a good targeting performance in 1997,
except the segment of non-MIG programs in 1997.
The minimum income guarantee, a means-tested
scheme to provide shield against deep poverty,
covers 8 percent of our relatively “generous” category of poor and targets 79
percent of the funds to those in need. The MIG scheme contributes to
22 percent to the consumption of the poor beneficiaries. The
coverage of the social assistance is rather limited, although it seemeds
to improve in 1997. In this year, one can distinguish between a well-targeted
MIG and the other programs, less targeted but with somewhathow
better coverage. With respect to (overall)
social assistance program efficacy, it reduced from 22 (30) percent
in 1995 (1996), to 6 percent in 1997, increasing
briefly to 30 percent in 1996.

Third, very isolated, is the child allowance scheme.
Designed as a
universal benefit, the child allowance scheme covers almost all families
with children. It reaches 60 percent of the poor families, the rest being the
pensioner
households or young families without children. Its targeting efficiency
varies, as expected, with the extent of poverty within Romania. In 1997, when
poverty was the highest in the period 1995-97, the program targeting was the
highest, at 51 percent, compared with 34 percent in 1996 (the lowest headcount
rate) and 42 percent in 1995 (the middle headcount rate). The change in the
level and formula of the benefit in 1997, although implemented only in March, has
as effect an increasedin the
program efficacy in helping the poor[xxi].
The program
increased its impact on the consumption of poor recipients share
of program benefit in the consumption of poor recipients raised from
6 or 7 percent of their total consumption in 1995-96,
to 11 percent in 1997. One would expect this indicator to perform better in
1998, a year of full program implementation.

From this overall picture,we canlet’s
summarize the cash benefit performance on one additional dimension –-
area of residence: recent studies emphasize tThe
high incidence of poverty in rural areaswas emphasized in
recent studies (World Bank 1997, Wagner et al. 1998, Dinculescu
and Chirca 1999, Chirca andTesliuc
1999). The rural poor outnumber the urban ones by a factor of two, and the
severity is higher in rural areas. Unlike inIn contrast with
the urban areas, the rural
inhabitants one
effectedturned
to a dramatic increase in informality, with a widespread increase
in unregistered self-entrepreneurship and a sharp reduction in the number of
wage earners. Also, rural areas suffers
from severe aging, comprisingconcentrating
70 percent of the country’s elderly andwhich
explainingsthea
larger share in pensions ((observed)).
However, the other categories of post-transfer poor are poorly served. The
unemployment benefit program denies unemployment benefits to those that were
laid-off with more than 2 (4) hectareshas
of land in plains (hills or mountains).

The above description of the Romanian
social safety net leads to the conclusion that almost all cash
transfers have a pro-poor targeting (except scholarships,
and some merit based benefits), but the coverage of the poor is, except for
child allowances, modest. The system addresses specific poverty risks, and many
poor households may fall between these risks, and will not be covered by the
social safety net. This raises the normative issue of the system’s fairness.
Much of the problem is caused because various programs use different poverty
lines, or none
at all, in assessing eligibility.

The only program that is pro-poor, the social aid program, uses a very
low definition for poverty, addressing, by
design, only the severe poverty situations.
At the beginning (August 1995), the program threshold was close to the
"extreme poverty line" used in this paper[xxii]
(around 85 percent of it). ThisSuch
line covers the poorest decile in 1995, 1997 and 1998. The program threshold
was eroded by inflation until the first quarter of 1997, when its real level
stabilized at about 50 percent of the "extreme poverty line"
(Fig.4.5).

The consequence of this
“erosion” ofthe program eligibility
criteria had dramatic effects on the population that might benefit from this
support. The program’s theoretical“theoretic” capacity
to cover the extreme poor dropped from the poorest decile to only 0.5 percent
of the poorest households. However, the authorities did not assessed
the consequence of the “non-indexation policy” on the poor, rushing instead to praisecount
on the plus side the lower spending on the program,
and the reduction ion
the number of beneficiaries. The effect of the measure on the poor was the
exclusion of 95% of the initial beneficiaries – those in “extreme poverty” –
from its payroll.

By 1997, the program outreach was 0.4 percent of the
households, close to its theoretical“theoretic”
coverage (0.5 percent). Our expectation that the program covers the extreme
poor was invalidated by the data.. Dhanji et al. (1999) estimate that in 1998
only 6.8 percent of those whose consumption is bellow the program threshold
benefited from social aid. Although 38 percent of recipients are in extreme
poverty, belonging to the poorest decile, only 15.6 percent have a lower
consumption than the "program official line". So,
the program has a high inclusion error.

In addition to the low coverage and weak capacity in
identifying the ultra-poor, the program (which is implemented and funded by the
local authorities) seems to have –- as
suggested by some field studies –- an
uneven territorial application. We believe that the current funding
arrangements for the program are a main cause offor
this result. From a normative point of
view, it would make more sense to centralizetransfer
funding responsibilities to central level. If program funding
were left to local resources, then poor communities would not be able to
generate as much resources as the richer ones, despite the fact that their
needs are higher. Poor communities would face difficult choices in the
composition of program expenditures. In practice, the (predictable)
opportunistic behavior of local administrators in poor communities aggravates
the situation. The program targets a marginal stratum of their constituencies,
the ultra-poor. Faced with the choice of servicing this group, or funding
benefits that most of the voters would capture (such as health and education,
roads, garbage collection and sewage), we were notdidnot
surprised to findus
that the second option was chosen[xxiii].
The program design failed to take into account “political economy”
considerations.

We suggest two design changes tTo
enhance program effectiveness in achieving its
goal, we suggest two
changes in program design: a) transfer at least the funding
responsibility from local to central level; and b) provide at least the necessary
funding necessary to cover at least the
“extremely poor”, and therebythus
transforming the social aid into a
veritable and secure component of the social safety net, able to
"catch" those (ultra)poor serviced inadequatelynot assisted or
insufficienthelped – or not at all –by other
programs. These recommendations are natural extensions of the previous remarks.
Inadequate funding in the past has generated either a tightening of the
program’s eligibility criteria, or arrears in the disbursement of the benefits.
BecauseAs
these funds goes to the most needy strata of the
population, those
who are unable to smooth their consumption over time through saving and
dis-saving, thise
lack of support in some periods will thrown
them into malnutrition, with damaging consequences for their future. Although
the payment’s arrears is a way local governments levy
“seniorage tax” from their constituencies, it is a pity that
such practices
hurts the ones in most need.

The
implementation of the second recommendation requires two actions: a revision of
the program eligibility criteria to cover the “extreme poor”, and an increase
in the program’s budget. CTo compliancey
with theour
definition of “extreme poverty” used in the study would entail,this implies an increase of the
benefit by 2.18 times. For instance, in December 1998, the line that would
cover the extreme poor would be 328,500 ROL for the first person, instead of
151,000 as provided by the law. As for the funding, this would be equivalent to
the extreme poverty deficit. In 1998, this amounted to 1,312 bBillion
ROL, compared with the 112 bBillion
ROL actually spent. This increase in the program-spending envelope by 11.7
times seems large. However, compared with the overall social budget (34,444 billionBln
ROL), this is a minor increase, amounting toof
less than four percent. Savings in some poorly-targeted
schemes can accommodate this necessary adjustment.

The effectiveness of a “welfare state” refers toconcerns
its ability to redistribute. In this section, we are asking
if the system of cash transfers and taxes existing in Romania redistribute or
not. In other words, is the Romanian state taking from the rich and transferring
to the poor? To
aThe answer to such
a question
requiresneeds
an exhaustive measurement of the transfers and public services received by the
households (or, dis-aggregated into specific group
of households), as well as the amount of gross income that is taxed (through
direct and indirect taxes, and social contributions).

The main purpose of thise
section is to analyze the impact of the fiscal and budgetary system on
households, by estimating the net flows (effect) between the household and
governmental sectors, by consumption decile, poverty status and area of
residence. We will build a “household sector account” with Government, able to
measure the pro-poor orientation of the whole fiscal and budgetary system. A
breakdown into (pre-transfer
but pension) consumption
deciles (before
all transfers except pensions) will provide robust measures of benefit
and tax incidence on various income strata.

Table 4.2 presents the 1997 household account with Government, by
consumption deciles. As apparent from the top section of the table, households
were ordered into deciles having an equal number of persons, based on their
“pre-transfer but pension” consumption, per adult equivalent. Thus, the first
decile will include the poorest 10 percent of the sample and the last decile
the richest 10 percent. As expected, larger families are clustered in the
poorest deciles, given the negative covariance between welfare level and family
size observed in almost all poverty or inequality studies. For instance, the
poorest 10 percent of Romanians are living in larger families, accounting for 6
percent of total number of families in the country. In contrast, the richest 10
percent live, in average, in smaller families. Their share in the total number
of families in Romania is 16 percent. The final row of the top section of the
table gives us the decile-distribution of our welfare indicator. In
distributional analyses, this information was used to discriminate between
weakly pro-poor to not-for-poor impact of a transfer. In 1997, the poorest
decile consumed 3 percent of total household consumption (before transfers but
pensions), contrasting with the 23 percent share of the richest
decile, of 23 percent.

The second section of the table presents, in a
tabular format, the benefits that flow from Government to households: cash
benefits like social insurance, entitlements and social assistance, and in-kind
benefits like health care and education services. The data in each column
represent the share of benefits received by that
population group as a share of thein
total budget of the program, were all benefits were first deflated in constant
prices. We will not do not here
an analyzeanalysisof cash benefits here, as
this was the topic ofa previous section. We highlight, however, a good anti-poverty
performance byof
the overall entitlements and social assistance systems, as opposed to “other
cash” items. Social insurance has the expected shape. We will comment, briefly,
on the distributional impact of the in-kind public services.

The central and local Government is providing a sizablesizeable
array of in-kind goods and services to households. However, in terms ofas
magnitude, almost all these outlays are spent for health care and education. WFrom
survey data, we measured the incidence of these services by
household type, using survey information on each service’s take-up. For
instance, the use of primary care services by the poorest decile is measured by
the share
of the first
decile’s
patients that used public dispensaries or polyclinics in the total number of
users in 1997. This share is considered equal with the share of public spending
on primary care that the poorest decile benefited from. For secondary health
care, we
approximated the share of the poorest decile in total spending was
approximated by using the percentage of the total
number of hospital days occupied by patients from the number
of days in hospitals of first decile’
patients in total number of hospital’ days. To
estimate the overall use of public health care services, the respective shares
were weighted by the public health care costs, by level of health care (primary
vs. secondary).

The incidence of education services by household
deciles is similar with the one observed in other countries: lower-level education goes primarily to low-income households, while
university education does not. In 1997, 17 (13) percent
of the pupils in primary and gymnasium (high school)
education were located in the poorest decile, compared with only 4
(5) percent in the richest one. In the same year 13 percent of pupils in high
school were from the lowest decile, compared with 5 percent from the richest.In contrast, the share of university students
from the poorest decile is only 4 percent, and of those from the richest one is
13 percent. Things are probably better, as our estimate for higher education’s
incidence is probably biased against the poor, due to poor information
collection[xxiv].

In contrast, the incidence of public health care
services is different than in other countries in the region. Some studies (provide
source) found that the poor tend to benefit from primary health
care, while secondary care is used disproportionately by the
rich. The estimates in Table 4.2 contradict thissuch
finding. In Romania, use of public secondary health care facilities is rather
uniformly distributed across deciles, while the rich use (public) primary
health care disproportionately. Sector studies (Tesliuc C, 1999) have drawn
a similar conclusion, and have offered as an explanation
the fact that a large share of the burden of disease is fromin
cardiovascular diseases,whichthat
are more commonfrequent
among rich than the poor. Also, the poor and the rich do not share disease
prevention patternsis
not similar for poor versus rich people, the latter one
having morea
pro-active, preventative behavior. Finally, as we measure the
“perceived” health status, we may face data quality problems.

The third section of the table summarizesd
the monetary flows from households to Government, in taxes and contributions.

The fourth section of Table 4.2 shows the net flows
between households and Government. Apart from inter-decile redistribution, the
Government levy throughin
taxes and contributions 8,053 billion ROL from the richest deciles
(fourth to tenth), out of which it redistributes
3,794 billion ROL to the poorest three deciles. Government net taxation amounts
to the difference between these two quantities,
or 4,260
bBillion
ROL. We will call this quantity 100 percent net tax, because it is net of
inter-decile redistribution. From households, the Government collects in taxes 189
percent of the net tax, redistributing 89 percentage points of it from the fourfourth
to the richest deciles to the first three
poorest deciles. Most of this redistribution occurs to the first (58 percentage
points out of 89) and second deciles (21 percentage points out of 89). The
answer to our initial question: “Does the Romanian state redistribute” is yes.

The last column in Table 4.2 gives the magnitude of
all these transfers, as a percentage of total consumption. In aggregate, the
Government is levieslevying
from households the equivalent of 52 percent of their consumption, and
transfers’ back 46 percent of the same
consumption figure. The net taxation represents slightly more than six percent
of total household consumption.

The provision of public goods seems to be well
targeted, except for higher education and secondary health care. This result is
quite common in all countries in the world. TPartly, this
is due in
part to the fact that observed because use
of these services is correlated with the income position of the beneficiaries
(endogeneity problem). One typical argument for the public provision of these
services is their associated positive externalities. However, these tend to
decrease with the level of the provision (they are greateraremore
for primary and secondary education,
than for the tertiary; and greateror
for primary care
thancomparedto secondary
care). Under tight fiscal constraints, a change in the provision mechanisms of
these benefits more inclined for partial cost-recovery should be investigated,
targeting (through means tests) the benefits to those unable to pay for them.

On the liability side, households contribute to the
state budget quite progressively. The most progressive instruments are the
property tax, the entrepreneurship income tax, and the VAT. Wage-related taxes and contributions are weakly pro-poor, as they
should be. As property is more unequally distributed than income, an increased
collection based on this factorsource
will be “welfarist”. The authors reccommend sSuch
a development
is recommended by the authors, given the low share of thesesuch
taxes in the overall fiscal burden of Romanianfaced by
households in Romania.

As a conclusion, one can say that the Romanian welfare system is highly
redistributive. This result is achieved by the combined distributional effect
of cash benefits, provision of public services, and also taxes and
contributions. The system improves the welfare of the first three deciles,
redistributing a part of the net taxation on the fourth to richest deciles
(Figure 4.5). Given the widespread poverty, such a feature is highly desirable.
A change in the system parameters from more to less redistribution would not be
opportune until growth will absorbs part of
the transient poverty.

This paper covers three new themes inof
the domestic debate on poverty, inequality and effectiveness of social policy
in curing poverty. First, it investigates the factors that may take poor out of
poverty.Second, it quantifies poverty
by duration
and,accentssignaling
the large share of transient poor, a social burden that will ease if the
economy can be placed again on a growth path.Finally, the paper assesses the effectiveness of the social safety net
in poverty alleviation.

The findings in each of the themes have direct
implications for a more effective social policy-making.
First and foremost, the paper confirms the centralprime
role education plays as a correlate of household welfare,
especially infor
the urban areas. Second, the paper diagnoses much of the existing
poverty as transient, and underscoressignals
the essential role of stabilization and growth. Third, the paper concludes that
the social safety net functions well in reducing inequality (meeting its redistribution
objective), but fails to cover the poor (its poverty alleviation objective). To
advance on the poverty alleviation front, the authors endorses
a proposal developed together with Dhanji et al. (1999) to
improve the operation of the social aid program, estimated to cost 0.33 percent
of GDP.

More generally, the analysis supports a
poverty-alleviation strategy that have as prime objectives:

·TtThe
completion of the reform agenda on structural adjustment and macrostabilization,
two factors that are essential prerequisites for the restoration of the
Romanian economy on a sustainable development path;

·TtThe
development of broad-based economic growth, by ensuring that the poor are able
to maximize the benefits from growth, by a) providing the necessary framework
for broad-based growth;b) continuing
the policies through which the poor gain access to essential assets, including
education;c) increasing the
productivity of the poor;and d) making
sure that markets do not discriminate against the poor;.

·TtThe
development of the human capital of the poor, as one of the keys for reducing
poverty. Special emphasis should be placed on early childhood development,
primary and gymnasium education. The differential in access to education among
areas of residence, of other characteristics, should be reduced;.

·TtThe
continuos improvement of the capacity of the social safety net toin
alleviateing
the poverty, especially among the permanent poor.

Not least, the authors would welcome a more open
dialogue on social and poverty issues, based on systematic analysis and
monitoring of these phenomenaon,
using all the “brainpower” existing in various places
in Romania today. We believe that a more transparent policy of
information dissemination – including here the
dissemination of the micro datasets that are the bread and butter of such
analysis – would be benefitcialfor the ones that matters:
the poor. The more access the research community can hasve
to such information, the better the chances that good poverty-alleviation
solutions willwould
be found. The more open such data and analysis are tocan
be used byallanyof
the stakeholders in Romania, the are better the chances that the deprived willwould
get the attention they deserve.

In this paper, we used five different methodologies of poverty
measurement.Three of them were used to
allow international comparisons:,such
as the absolute poverty lines of
one US$ a
day;,or four
international PPP$ a day, as used in section one of
chapter onefirst chapter, section one;,andor
the line pioneered by the World Bank for Romania 1989-1994. The other two
lines, based on a modified version of the relative method of poverty
measurement,were used in the domestic context, as they reflect better Romanian
realities and social policy goals.

Romania does not have an official poverty line.
However, recent studies used systematically similar
methodologies for poverty measurement systematically (Wagner et al. 1998,
Dinculescu and Chirca 1999, ChircaChirc_
and TesliucTe_liuc
1999), building a national “expert consensus”. As the Presidential Commission
against Poverty commissioned some of the studies, this “expert consensus”
approaches an official acceptance or even support. The core poverty analysis
undertaken in this study is done against the same poverty benchmark and
methodology as the one used in the above-mentioned studies.

We measured household welfare by the level of (current)
consumption of the members of a household[xxv].
The consumption indicator includes all food, non-food and services consumed by
the household members during a month, both from purchases and from own
production (self-consumption). This indicator does not include imputed
consumption of durables or owner-occupied houses, due to the lack of reliable
rental prices for these assets, a consequence of their shallow rental
markets. To compare the welfare of households of different sizes, consumption
was divided by the number of “adult equivalents” existing in the household,
using a scale derived by Romanian nutritionists (Table A2.1). The resulting
indicator, “consumption per adult equivalent”, was used to rank the households
according to their welfare.

All persons living in households with a lower level
of consumption per adult equivalent than the poverty line were counted as
poor.The poverty line used in
this study is defined as 60 percent of the monthly average consumption per adult
equivalent in 1995, equivalent to US$USD
40.

In addition to this core poverty benchmark, we used
three other “lines”, one to signal “extreme” poverty in the Romanian context,
and two for international comparisons:

ØTo
signal “extreme poverty”, we used a line defined as 40 percent of the
average consumption per adult equivalent in 1995, equivalent to US$USD
27 per month.

ØTo
signal the scope of mal-nutrition, we used “the PPP$1 a
day” per capita poverty line, for which comparable country poverty estimates
are found in World Development Indicators 1999. The reference PPP$ used for
international comparisons was the 1985 international PPP$.

ØFor
regional comparisons with other Europe and Central Asia (ECA) countries,
we used a 4 PPP$4 per day
per capita poverty line, computed for the 1990 international PPP$.

We based our poverty measures on household
consumption, not on income. We have considered household consumption
expenditure a more reliable measure than income,
for two reasons.First, it reflects better
permanent income better, particularly with the LSMS data we
are working with. Romanian LSMS useshas
the month as its
reporting period, and structural features of the Romanian
economy makes food self-/consumption
an important consumption source. Food self-consumption is derived from
agricultural income, usually obtained during summer and fall months.
With the reporting period set at one month, the survey data do not capture
agricultural income, but do measure instead self-consumption
quite accurately. Second, reporting problems may arise with income when asking
people to share information about it.Evidence from nine countries in Eastern Europe and Former Soviet Union
confirms
that HBS expenditures tend to be higher than HBS income (Milanovic, 1998).

The survey data do not allow us to distinguish between
intra-household allocation. Subsequently, our poverty analysis
assumes that this allocation is fair, and – for food consumption – is
proportional with the nutritional needs of each member, known to vary with age,
sex and occupation. The adult equivalent scale used to “standardize” these
needs is based on the “normative” caloric intakes recommended by Romanian
nutritionists.

To measure the scope, or
incidence of poverty, we have used the following
indicators: the number of poor and the headcount index. To measure the severity
or depth of poverty, we used the following indicators: the consumption
shortfall, the poverty deficit, the poverty gap index, poverty severity index
and the Gini index. To signal the stability of poverty in time, we computed its
elasticity with respect to GDP.

This report defines the poor as the individuals with
consumption expenditure per adult equivalent below 70,972 ROL (in January 1995
prices) per month.This threshold
represents 60 percent of the average consumption expenditure per adult
equivalent.The same poverty line has
been used for all 4 years we are analyzing.

To compare the rural-urban poverty rate and its
year-on-year variations we inflated the 1995 relative poverty line for 40
percent and 60 percent of the average consumption expenditure per adult
equivalent using the NCS equivalence scale. All the members of the households
under this line were regarded as poor.

Since earlier papers (World Bank, 1997) reported
consistently lower food prices in rural areas,that would requiringe
deflator corrections by area, we tested this assumption. We determined the
average household food consumption in (unit
value) prices by area. For 1996, the
monthly food basket was three to six per cent lower in rural than in urban area
in terms of money – a difference which we did not regard as significant. First,
because we used unit values not prices to estimate the purchasinge
power differential by area. Given the direct proportionality between price and
quality, the lower unit value in rural areas may be linked to a somewhat poorer quality.
Second, there are goods that are seldom bought in rural areas where self-own
consumption is quite commonfrequent.
Unfortunately, accuratelydetermined unit
values for such goods are not available.